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  {
   "source": [
    "# BP神经网络预测\n",
    "\n",
    "这里根据师姐的论文和matlab代码，利用python重写了一下，简单的建立一个三层的神经网络，即除却输入层和输出层就只用一层隐藏层"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入包\n",
    "import torch\n",
    "from torch import nn\n",
    "import numpy as np\n",
    "import torch.nn.functional as Fun\n",
    "import scipy.io as scio\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "source": [
    "## 读取数据\n",
    "\n",
    "数据是提取师姐处理后的血糖数据，数据量较小，仅有200多个数据\n",
    "\n",
    "* `ppp`是用于预测的特征，分别为*收缩压*、*脉率(心率)*、*(体温)*、*吸光度*\n",
    "* `TTT`即对应特征的血糖值"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = \"data.mat\"\n",
    "data = scio.loadmat(file_name)\n",
    "# PPP标签的是用于预测血糖的四个特征，作为features\n",
    "x = data.get(\"PPP\")\n",
    "x = x.T\n",
    "# TTT标签是血糖数据，作为labels\n",
    "y = data.get(\"TTT\")\n",
    "y = y[0].reshape(len(y[0]),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "x.shape : (205, 4)  y.shape:  (205, 1)\n"
     ]
    }
   ],
   "source": [
    "print(\"x.shape :\", x.shape, \" y.shape: \", y.shape)"
   ]
  },
  {
   "source": [
    "确保`x`和`y`的第一维的维度相同，且`y.shape`为`(205, 1)`而不是`(205, )`\n",
    "\n",
    "分别查看`x`和`y`的部分数据"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[ 9.53333333e+01  7.73333333e+01  3.67333333e+01 -7.13518730e-01]\n [ 1.04333333e+02  5.80000000e+01  3.32000000e+01 -6.43337135e-01]\n [ 9.50000000e+01  7.30000000e+01  3.55000000e+01  9.59860109e-02]\n [ 9.40000000e+01  7.10000000e+01  3.51000000e+01  3.01645487e-01]\n [ 1.03000000e+02  6.73333333e+01  3.64666667e+01 -4.38108606e-02]]\n"
     ]
    }
   ],
   "source": [
    "print(x[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[4.6]\n [6.6]\n [9.1]\n [5.1]\n [5.3]]\n"
     ]
    }
   ],
   "source": [
    "print(y[0:5])"
   ]
  },
  {
   "source": [
    "## 数据预处理\n",
    "\n",
    "这里主要做两个工作\n",
    "\n",
    "* 归一化\n",
    "* 将`numpy.array`类型的数据转换为`tensor`\n",
    "\n",
    "神经网络重要的一点就是在训练之前对数据进行预处理，这样做的好处有以下几点：\n",
    "\n",
    "* 预处理可以加快训练速度\n",
    "* 在神经网络中使用了激活函数(activation function)，不进行数据归一化，激活函数会很快趋于平坦期\n",
    "\n",
    "> 之前没注意到数据归一化，结果神经网络训练训练之后输出基本一致，一定要进行预处理归一化"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "x_mean = np.mean(x,axis=0)\n",
    "print(x_mean)\n",
    "x_std = np.std(x,axis=0)\n",
    "# 特征归一化处理\n",
    "x = (x-x_mean) / x_std\n",
    "print(x_std)\n",
    "# 将numpy转换为tensor\n",
    "x = torch.tensor(x, dtype = torch.float32)\n",
    "y = torch.tensor(y, dtype = torch.float32)"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[99.58211382 70.27642276 35.3295935  -0.13151976]\n[8.14764866 9.24648983 1.04861871 0.43334363]\n"
     ]
    }
   ]
  },
  {
   "source": [
    "查看归一化处理后的数据"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([[-0.5215,  0.7632,  1.3387, -1.3430],\n        [ 0.5831, -1.3277, -2.0309, -1.1811],\n        [-0.5624,  0.2946,  0.1625,  0.5250],\n        [-0.6851,  0.0783, -0.2189,  0.9996],\n        [ 0.4195, -0.3183,  1.0844,  0.2024]])\n"
     ]
    }
   ],
   "source": [
    "print(x[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([[4.6000],\n        [6.6000],\n        [9.1000],\n        [5.1000],\n        [5.3000]])\n"
     ]
    }
   ],
   "source": [
    "print(y[0:5])"
   ]
  },
  {
   "source": [
    "## 创建训练数据集和测试数据集\n",
    "\n",
    "这里主要师姐创建的数据集都是正常的数据集，且数据集较小，就没有使用验证集，简单的将数据集分为训练集和测试集"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_train_test(X, y, test_ratio):\n",
    "    shuffled_indices = np.random.permutation(len(y))\n",
    "    test_set_size = int(len(y) * test_ratio)\n",
    "    test_indices = shuffled_indices[:test_set_size]\n",
    "    train_indices = shuffled_indices[test_set_size:]\n",
    "    return X[train_indices,:], y[train_indices], X[test_indices,:], y[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练集和测试集分割比例为6:4\n",
    "train_x, train_y, test_x, test_y = split_train_test(x, y, 0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(torch.Size([123, 4]),\n",
       " torch.Size([123, 1]),\n",
       " torch.Size([82, 4]),\n",
       " torch.Size([82, 1]))"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "train_x.shape, train_y.shape, test_x.shape, test_y.shape"
   ]
  },
  {
   "source": [
    "为了训练更快，这里使用了mini-batch训练方法\n",
    "\n",
    "训练集5条合为一个batch，并打乱训练集，合并为train_iter，后续可以迭代解包获得features和labels，即`x`和`y`"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = torch.utils.data.TensorDataset(train_x, train_y)\n",
    "train_iter = torch.utils.data.DataLoader(dataset, 5 ,shuffle=True)"
   ]
  },
  {
   "source": [
    "## 定义BP神经网络\n",
    "\n",
    "这里定义的是一个三层的后向反馈神经网络(Back Propagation Neural Network, BPNN)，4层输入层，即每个输入有四个特征，按照以下公式：\n",
    "\n",
    "$$ N_{out} = 2N_{in} + 1 $$\n",
    "\n",
    "这里隐藏层定义为9层，因为这里做的是回归，预测血糖值，所以输出层定义为1层\n",
    "\n",
    "优化器使用常见的Adam优化算法，误差代价计算函数使用`MSELoss`"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(torch.nn.Module):\n",
    "    def __init__(self, n_features, n_hidden, n_output):\n",
    "        super(Net, self).__init__()\n",
    "        self.hidden = torch.nn.Linear(n_features, n_hidden)\n",
    "        self.out = torch.nn.Linear(n_hidden, n_output)\n",
    "\n",
    "    def forward(self, X_data):\n",
    "        x = self.hidden(X_data)\n",
    "        x_relu = Fun.tanh(x)\n",
    "        predict_y = self.out(x_relu)\n",
    "        return predict_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_rmse(net, features, labels):\n",
    "    with torch.no_grad():\n",
    "        clipped_preds = torch.max(net(features), torch.tensor(1.0))\n",
    "        rmse = torch.sqrt(2 * loss(clipped_preds.log(),labels.log()).mean())\n",
    "    return rmse.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = Net(4, 10, 1)\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=0.0005)\n",
    "loss = torch.nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练1000次，这个可以调小一点，看之后的训练误差，差不多500次左右就收敛了\n",
    "train_ls, test_ls = [], []\n",
    "for i in range(150):\n",
    "    for x, y in train_iter:\n",
    "        out = net(x)\n",
    "        l = loss(out, y)\n",
    "        optimizer.zero_grad()\n",
    "        l.backward()\n",
    "        optimizer.step()\n",
    "    train_ls.append(l)\n",
    "    if test_ls is not None:\n",
    "        test_ls.append(loss(net(test_x), test_y))"
   ]
  },
  {
   "source": [
    "查看训练误差和测试误差检查模型是否过拟合或欠拟合\n",
    "\n",
    "训练误差噪声较大的原因是使用的mini-batch的方法，测试误差较平坦是因为计算测试误差是使用的是所有的测试集"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 绘制训练误差\n",
    "plt.plot(train_ls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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YM6s3s/qGhoZ2bq7CmcGvfw1vvw2TJsVOIyIpkfeHmO7uQKunSLh7nbvXuHtNdXV1vpurXCNGwBFHwH//NyxdGjuNiKRAewv8XTPrC5B8XV64SNKqK66ANWvCjIUiUvHaW+APAqOTx6OBBwoTRzZo++3DTR+mTIFnnomdRkQiy+U0wjuAvwI7mdkSMxsD/BoYaWavA/snz6UUJk4Ml9ifeaZOKxSpcJ3aeoO7H9PKS/sVOIvkYrPN4PLL4Zhj4Oab4ZRTYicSkUh0JWYWHXUUfPvb4eKe99+PnUZEIlGBZ5EZXH01/POfYcIrEalIKvCs2mWXcBPk66+H+vrYaUQkAhV4ll1ySbh/5o9/rA80RSqQCjzLevSAK68MR+DXXRc7jYiUmAo8644+Gg46KJxe+OabsdOISAmpwLPODOrqoGNHGDMG1q2LnUhESkQFXg769w9DKU89BTfcEDuNiJSICrxcjBkDI0fChAmweHHsNCJSAirwcmEGN90EHTqEMtc9NEXKngq8nAwYEC6znzZNZ6WIVAAVeLkZOzaclXLOOTB7duw0IlJEKvByYwa33gpVVWHOlI8/jp1IRIpEBV6Ott46zBn+2mtQWxs7jYgUiQq8XO27L1xwAdx2WzgiF5GyowIvZxdeCPvsA+PGwauvxk4jIgWmAi9nHTuGoZRu3eDII+Gjj2InEpECUoGXu223hbvugoULYfRoXWovUkZU4JVgxIhwfvj994cpaEWkLLR5T0wpE2edBXPmwMUXh5sin3xy7EQikicVeKVonLVw2TI47TTo0wcOPTR2KhHJg4ZQKknnznDPPTBsGPzwh/Dcc7ETiUgeVOCVpnt3eOQR2GabcAS+cGHsRCLSTirwSrT11vCnP4XTDA88EN55J3YiEWmHvArczBab2Stm9rKZ6dboWbLDDvDoo7BiBYwaBStXxk4kIhupEEfg+7r7UHevKcDvJaW0225w330wbx4cdhh8+GHsRCKyETSEUukOOCBcrfnXv8L++8N778VOJCI5yrfAHXjczGaZ2dhCBJIIfvjDcCQ+Zw5897vhVEMRSb18C/xb7r4rcDBQa2bfaf4GMxtrZvVmVt/Q0JDn5qRoDjssjIm/9RZ85zvwt7/FTiQibcirwN19afJ1OXA/sHsL76lz9xp3r6murs5nc1JsI0bAk0+GDzb33hteeSV2IhHZgHYXuJltZmabNz4GDgDmFiqYRDJ8OPz5z+Hxt74FU6fGzSMircrnCLw38LSZzQaeBx5x9z8WJpZE9c1vhg81+/cP54lfeqlmMRRJoXbPheLubwK7FDCLpEn//vDss3DKKTBxIsyYEe7s06tX7GQiktBphNK6LbaAO++Ea68NY+PDhsEzz8ROJSIJFbhsmFm4Jduzz8Imm4RbtF1+uYZURFJABS652W03ePFFOOIImDAhTIT197/HTiVS0VTgkrsePeDuu+Hqq+Gpp2DnncMHnF98ETuZSEVSgcvGMYPTT4cFC8Jl+BMnwtChMH167GQiFUcFLu2z3XbhHpsPPwyffRYuAjrqKHj77djJRCqGClzyc8ghYTbDiy+GBx+Er30t3H9Tc4yLFJ0KXPLXtStceCG8+mo4Cr/mGhg0KJy9ojlVRIpGBS6Fs912cMst8NprMHo03HQT7LgjHHtsuP+me+yEImVFBS6Ft/32UFcHb7wRPvB85BHYc0/YYw+4/XZYtSp2QpGyoAKX4hkwACZNgiVLwtWcH34IJ5wAvXvDcceFYl+9OnZKkcxSgUvxbb55GA+fPx+mTYOjjw5zjx96KPTpE0r9zjvh/fdjJxXJFPMSjkvW1NR4fb3ufSyEi38efxzuugseeyzcyq1DB9hrL9hvv3BTieHDoVu32ElFojOzWS3dd1gFLvGtXQsvvBCGVB57DF56Kcy10rkz1NSEy/i/8Y31S1VV7MQiJaUCl+xYuTJMnjVjBvzlL+FenR99tP71Pn1CkQ8Z8uVi79kzXmaRIlKBS3a5hys858378jJ/Pnzyyfr39eoV5jEfMCB8bXy8zTbhtV69wtF7p3ZPgy8SRWsFrn/Jkn5moYgHDICDD16/ft26MCNiY6EvWhSKftGiMNnWypUt/35bbrm+zHv0CPOeb755+Nr4eLPNwvh7167ha9Ola9cwvNN06dTpy4/NSrFnpMKpwCW7OnSAgQPDcsghX339ww9DoS9bFj4kXbEiLI2P33svDM288054b+NSiP+Vduy44ZJv7XmnTuHP1drSseOGX8/yUqw/m1lujzNIBS7la4st1o+P52rdunCh0apV8Omn6x83f7569ZeXNWvye964fPpp+AGybl1Y1q5d/7hYiwSNhd602DdU+hv7vK4u3Ci8gFTgIk116ADdu4elUjT9gRFjKdYPqbVr1//Zmv4Zc3nc1vta+7039LwI/6ZU4CKVziwMX3TsGDuJbCRdiSkiklEqcBGRjFKBi4hklApcRCSj8ipwMzvIzBaa2RtmNrFQoUREpG3tLnAz6whcCxwMDAGOMbMhhQomIiIbls8R+O7AG+7+prt/AdwJHF6YWCIi0pZ8Cnxb4O0mz5ck677EzMaaWb2Z1Tc0NOSxORERaaroF/K4ex1QB2BmDWbW3tuU9wJWFCxYcShjYShj/tKeD5RxY2zX0sp8Cnwp0L/J837Jula5e3V7N2Zm9S1Np5gmylgYypi/tOcDZSyEfIZQXgAGm9kgM9sEOBp4sDCxRESkLe0+Anf3NWZ2OvAnoCNws7vPK1gyERHZoLzGwN39UeDRAmVpS12JtpMPZSwMZcxf2vOBMuatpLdUExGRwtGl9CIiGaUCFxHJqEwUeNrmXDGz/mY23czmm9k8MxufrK8ysyfM7PXka88UZO1oZi+Z2cPJ80FmNjPZl3clZxDFzLelmd1jZq+a2QIz2zNt+9HMzk7+nuea2R1mtmns/WhmN5vZcjOb22Rdi/vNgv9Jss4xs10jZrw8+bueY2b3m9mWTV47N8m40MwOjJWxyWvnmJmbWa/keZT9uCGpL/CUzrmyBjjH3YcAw4HaJNNEYKq7DwamJs9jGw8saPL8UmCSu+8IvA+MiZJqvauAP7r714FdCFlTsx/NbFvgTKDG3f+FcMbV0cTfj7cABzVb19p+OxgYnCxjgesjZnwC+Bd3/ybwGnAuQPL9czTwjeTXXJd878fIiJn1Bw4A/t5kdaz92Dp3T/UC7An8qcnzc4FzY+dqlvEBYCSwEOibrOsLLIycqx/hG3kE8DBghKvKOrW0byPk6wG8RfJhepP1qdmPrJ8yoopw1tbDwIFp2I/AQGBuW/sN+F/gmJbeV+qMzV77PjAlefyl72vC6cl7xsoI3EM4oFgM9Iq9H1tbUn8ETo5zrsRiZgOBYcBMoLe7L0te+gfQO1auxG+BCUDjrce3Aj5w9zXJ89j7chDQAPwuGea5ycw2I0X70d2XAr8hHIktA1YCs0jXfmzU2n5L6/fQj4DHksepyWhmhwNL3X12s5dSk7FRFgo8tcysO3AvcJa7f9j0NQ8/oqOdo2lmhwLL3X1WrAw56ATsClzv7sOAT2g2XJKC/diTMMvmIGAbYDNa+C932sTeb20xs/MJQ5FTYmdpysy6AecBF8bOkossFPhGz7lSCmbWmVDeU9z9vmT1u2bWN3m9L7A8Vj5gb+B7ZraYMNXvCMJ485Zm1ngBV+x9uQRY4u4zk+f3EAo9Tftxf+Atd29w99XAfYR9m6b92Ki1/Zaq7yEzOxE4FPiP5AcNpCfjDoQf1rOT751+wItm1of0ZPx/WSjw1M25YmYGTAYWuPuVTV56EBidPB5NGBuPwt3Pdfd+7j6QsM+muft/ANOBHyRvi53xH8DbZrZTsmo/YD4p2o+EoZPhZtYt+XtvzJia/dhEa/vtQeCE5CyK4cDKJkMtJWVmBxGG9b7n7quavPQgcLSZdTGzQYQPCp8vdT53f8Xdt3b3gcn3zhJg1+Tfamr24/+LOQC/ER8yjCJ8Yr0IOD8Feb5F+O/pHODlZBlFGGOeCrwOPAlUxc6a5P0u8HDyeHvCN8YbwN1Al8jZhgL1yb78A9AzbfsRuBh4FZgL3A50ib0fgTsIY/KrCSUzprX9Rvjw+trk++cVwhk1sTK+QRhHbvy+uaHJ+89PMi4EDo6Vsdnri1n/IWaU/bihRZfSi4hkVBaGUEREpAUqcBGRjFKBi4hklApcRCSjVOAiIhmlAhcRySgVuIhIRv0fKCJ4OZELcIAAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 绘制测试误差\n",
    "plt.plot(test_ls, c='r')\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## 预测测试集并校验预测结果准确率\n",
    "\n",
    "为了更专业地估计血糖预测值相较于参考值的临床准确值，1978年clark等人提出了克拉克误差网络分析(Clarke error grid analysis, EGA)方法，对血糖浓度检测方法进行估计。如今，该方法以广泛被认为是评估血糖浓度检测产品临床准确性的“金标准”。其中X轴和Y轴分别表示血糖浓度的参考值和预测值，即分别为真值和预测值，根据不同的准确性以及导致的临床后果的严重程度，克拉克误差网络分为A，B，C，D和E共5个区域。\n",
    "\n",
    "区域A表示偏离参考值约20%或在低血糖范围内(<70mg/dl)的血糖浓度预测值，该该范围内的预测值是准确的，因此可以据此做出正确的临床治疗方案。区域B位于区域A的上方和下方，该区域的预测值偏离参考值超过20%，但只会导致良性治疗决策或无决策。落在A区域和B区域的预测值在临床上都是可以接受的，而落在区域C，D和E的预测值具有潜在的危险，并且有可能会导致重大的临床错误。因此，可以分别通过落在区域A，B，C，D和E中的样本数据的百分比来评估已建立的校准模型的性能。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[6.503653 ],\n",
       "       [6.264736 ],\n",
       "       [5.713034 ],\n",
       "       [6.313847 ],\n",
       "       [5.6647844]], dtype=float32)"
      ]
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "# 获取预测结果，记住将tensor转换为numpy\n",
    "predict_y = net(test_x).detach_().numpy()\n",
    "predict_y[0:5]"
   ]
  },
  {
   "source": [
    "克拉克网络的函数参照师姐的matlab程序修改得到，或许可以简化其中的一些循环，不过确实有点麻烦，就没优化了。。。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clarke网格误差分析\n",
    "def clarke(y,y_predicted):\n",
    "    \"\"\"\n",
    "    clarke网格分析\n",
    "\n",
    "    total, percentage = clarke(y, yp)\n",
    "    \n",
    "    INPUTS:\n",
    "    y       Reference values(血糖浓度参考值)\n",
    "    yp      Predicted/estimated values(血糖预测值)\n",
    "    \n",
    "    OUTPUTS:\n",
    "    total   A，B，C，D，E五个区域落入点的总数，为一个(5, 1)的np.array\n",
    "    percentage  分别是五个区域落入点数占总数的百分比\n",
    "    \"\"\"\n",
    "    # 设置坐标轴范围和坐标轴名称\n",
    "    plt.xlim(0, 400*0.0556)\n",
    "    plt.ylim(0, 400*0.0556)\n",
    "    plt.xlabel('Reference values of Blood Glucose(mmol/L)')\n",
    "    plt.ylabel('Predicted values of Blood Glucose(mmol/L)')\n",
    "    # 获取数据长度\n",
    "    n = len(y)\n",
    "    # 散点图绘制数据\n",
    "    plt.scatter(y, y_predicted, s=15)\n",
    "    # 绘制网线\n",
    "    # 上半区网线\n",
    "    plt.plot([0,400*0.0556],[0,400*0.0556],linestyle=':',c='k') \n",
    "    plt.plot([0,175*0.0556/3],[70*0.0556,70*0.0556],c='k',linewidth=1)\n",
    "    plt.plot([175*0.0556/3,400*0.0556/1.2],[70*0.0556,400*0.0556],c='k',linewidth=1)\n",
    "    plt.plot([70*0.0556,70*0.0556],[84*0.0556,400*0.0556],c='k', linewidth=1)\n",
    "    plt.plot([0,70*0.0556],[180*0.0556,180*0.0556],c='k',linewidth=1)\n",
    "    plt.plot([70*0.0556,290*0.0556],[180*0.0556,400*0.0556],c='k', linewidth=1)\n",
    "    # 下半区网线绘制\n",
    "    plt.plot([70*0.0556,70*0.0556],[0,56*0.0556],c='k',linewidth=1)\n",
    "    plt.plot([70*0.0556,400*0.0556],[56*0.0556,320*0.0556],c='k',linewidth=1)\n",
    "    plt.plot([180*0.0556,180*0.0556],[0,70*0.0556],c='k',linewidth=1)\n",
    "    plt.plot([180*0.0556,400*0.0556],[70*0.0556,70*0.0556],c='k',linewidth=1)\n",
    "    plt.plot([240*0.0556,240*0.0556],[70*0.0556,180*0.0556],c='k',linewidth=1)\n",
    "    plt.plot([240*0.0556,400*0.0556],[180*0.0556,180*0.0556],c='k',linewidth=1)\n",
    "    plt.plot([130*0.0556,180*0.0556],[0,70*0.0556],c='k',linewidth=1)\n",
    "    \n",
    "    # 绘制区域标签A,B,C,D,E\n",
    "    plt.text(30*0.0556,20*0.0556,'A',fontsize=9)\n",
    "    plt.text(30*0.0556,150*0.0556,'D',fontsize=9)\n",
    "    plt.text(30*0.0556,380*0.0556,'E',fontsize=9)\n",
    "    plt.text(150*0.0556,380*0.0556,'C',fontsize=9)\n",
    "    plt.text(160*0.0556,20*0.0556,'C',fontsize=9)\n",
    "    plt.text(380*0.0556,20*0.0556,'E',fontsize=9)\n",
    "    plt.text(380*0.0556,120*0.0556,'D',fontsize=9)\n",
    "    plt.text(380*0.0556,260*0.0556,'B',fontsize=9)\n",
    "    plt.text(280*0.0556,380*0.0556,'B',fontsize=9)\n",
    "\n",
    "    # 计算数据\n",
    "    total = np.zeros(5)\n",
    "    # A区域\n",
    "    for i in range(0,n):\n",
    "        if (y[i] <= 70*0.0556 and y_predicted[i] <= 70*0.0556) or (y_predicted[i] <= 1.2*y[i] and y_predicted[i] >= 0.8*y[i]): \n",
    "            total[0] = total[0] + 1\n",
    "        else:\n",
    "            # E区域\n",
    "            if(y[i] >= 180*0.0556 and y_predicted[i] <= 70*0.0556) or (y[i] <= 70*0.0556 and y_predicted[i] >= 180*0.0556):\n",
    "                total[4] = total[4] + 1\n",
    "            else:\n",
    "                # 区域C\n",
    "                if (y[i] >= 70*0.0556 and y[i] <= 290*0.0556) and (y_predicted[i] >= y[i] + 110*0.0556) or (y[i] >= 130*0.0556 and y[i] <= 180*0.0556) and (y_predicted[i] <= (7/5)*y[i] - 182*0.0556):\n",
    "                    total[2] = total[2] + 1\n",
    "                else:\n",
    "                    # 区域D\n",
    "                    if (y[i] >= 240*0.0556) and (y_predicted[i] >= 70*0.0556) and (y_predicted[i] <= 180*0.0556) or (y[i] <= 175*0.0556/3 and y_predicted[i] <= 180*0.0556) and (y_predicted[i] >= 70*0.0556) or (y[i] >= 175*0.0556/3 and y[i] <= 70*0.0556) and (y_predicted[i] >= (6/5)*y[i]):\n",
    "                        total[3] = total[3] + 1\n",
    "                    else:\n",
    "                        # 区域B\n",
    "                        total[1] = total[1] + 1\n",
    "\n",
    "    percentage = (total/n)*100\n",
    "    plt.show()\n",
    "    return total, percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_y = test_y.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "test_y.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "predict_y.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 绘制克拉克误差网络\n",
    "total, percentage = clarke(test_y, predict_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[5.3]\n [5.3]\n [5.6]\n [6.2]\n [4.4]]\n"
     ]
    }
   ],
   "source": [
    "print(test_y[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[6.503653 ]\n [6.264736 ]\n [5.713034 ]\n [6.313847 ]\n [5.6647844]]\n"
     ]
    }
   ],
   "source": [
    "print(predict_y[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([54., 28.,  0.,  0.,  0.])"
      ]
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "source": [
    "# 查看落入各区域的点数\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([65.85365854, 34.14634146,  0.        ,  0.        ,  0.        ])"
      ]
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "source": [
    "# 查看落入各区域点数的百分比\n",
    "percentage"
   ]
  },
  {
   "source": [
    "## 查看预测值与真实值的差别\n",
    "\n",
    "那个克拉克误差网络看起来确实不太好看预测与真实值的差别，还是习惯直接看两个值之间的差别，将预测值减去真实值，并绘制一个柱状图，这样看起来就简单明了了\n",
    "\n",
    ">plt.bar()绘图居然提示报错。。。将numpy.array转换为list才成功绘图。。。这个bug有点莫名其妙，暂时不清楚为啥，有知道的可以告诉我一下"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[[-1.2036528587341309],\n",
       " [-0.9647359848022461],\n",
       " [-0.11303424835205078],\n",
       " [-0.11384725570678711],\n",
       " [-1.264784336090088]]"
      ]
     },
     "metadata": {},
     "execution_count": 29
    }
   ],
   "source": [
    "# 将numpy.array转换为list，便于plt.bar画图\n",
    "diff = (test_y - predict_y).tolist()\n",
    "diff[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "# 将嵌套的数组转换为一维的列表\n",
    "diff = list(itertools.chain.from_iterable(diff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     "metadata": {
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    }
   ],
   "source": [
    "plt.bar(range(len(diff)), diff)\n",
    "# plt.ylim(-10, 10)\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "这里基本上可以看到，预测值和真实值的差距真的是太大了。。。。血糖值正常在6左右，误差基本在1-3，甚至有3以上的\n",
    "\n",
    "这里总结一下可以改进的地方吧：\n",
    "\n",
    "* 决定血糖的可以加上其他的因素，例如测试者的年龄，体重，身高(或者BMI指数，胖的人多半血糖高)等，只要是可能和测试者血糖相关的都可以\n",
    "* 模型的复杂度不够，相关的也意味着数据的特征维度不够\n",
    "* 数据量不够，这里数据量只有205组，确实太少太少了\n",
    "* 还有一点比较麻烦的是，人与人的体质是不同的，测试的模型可以分为单人单次的模型或是多人多次的模型，如果是多人多次测量的数据可能包含个人的误差，要保证准确度，需要考虑个体差异, 这点有待改进"
   ],
   "cell_type": "markdown",
   "metadata": {}
  }
 ]
}